accurate {aTSA} R Documentation

## Accurate Computation

### Description

Computes the accurate criterion of smoothed (fitted) values.

### Usage

accurate(x, x.hat, k, output = TRUE)


### Arguments

 x a numeric vector of original values. x.hat a numeric vector of smoothed (fitted) values. k the number of parameters in obtaining the smoothed (fitted) values. output a logical value indicating to print the results in R console. The default is TRUE.

### Details

See http://www.dms.umontreal.ca/~duchesne/chap12.pdf in page 616 - 617 for the details of calculations for each criterion.

### Value

A vector containing the following components:

 SST the total sum of squares. SSE the sum of the squared residuals. MSE the mean squared error. RMSE the root mean square error. MAPE the mean absolute percent error. MPE the mean percent error. MAE the mean absolute error. ME the mean error. R.squared R^2 = 1 - SSE/SST. R.adj.squared the adjusted R^2. RW.R.squared the random walk R^2. AIC the Akaike's information criterion. SBC the Schwarz's Bayesian criterion. APC the Amemiya's prediction criterion

### Note

If the model fits the series badly, the model error sum of squares SSE may be larger than SST and the R.squared or RW.R.squared statistics will be negative. The RW.R.squared uses the random walk model for the purpose of comparison.

Debin Qiu

### Examples

X <- matrix(rnorm(200),100,2)
y <- 0.1*X[,1] + 2*X[,2] + rnorm(100)
y.hat <- fitted(lm(y ~ X))
accurate(y,y.hat,2)


[Package aTSA version 3.1.2 Index]